COGS 209Statistical Machine Learning for Social Sciences
Advanced methods and fundamental principles for learning from data. Emphasis is on foundational methods in supervised and unsupervised machine learning problems, covering trade-offs inherent in model design, training, evaluation, selection, and validation. Students will not receive credit for both COGS 209 and CSS 206. Recommended preparation: introductory statistics and familiarity with scientific programming and analysis software (MATLAB, Python, or R).
Prerequisites
From the catalog: “graduate standing.”